id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-267055-xscwk74r	Chassagnon, Guillaume	AI-Driven quantification, staging and outcome prediction of COVID-19 pneumonia	2020-10-15		.txt	text/plain	5146	248	43	Our approach relies on automatic deep learning-based disease quantification using an ensemble of architectures, and a data-driven consensus for the staging and outcome prediction of the patients fusing imaging biomarkers with clinical and biological attributes. • A Covid-19-specific holistic, highly compact multi-omics signature integrating imaging/clinical/ biological data and associated comorbidities for automatic patient staging is presented and evaluated. Our approach relies on automatic deep learning-based disease quantification using an ensemble of architectures, and a datadriven consensus for the staging and outcome prediction of the patients fusing imaging biomarkers with clinical and biological attributes. In this study, we investigated an automatic method ( To the best of our knowledge this is among a few systematic efforts to quantify disease extent, to discover low dimensional and interpretable imaging biomarkers and to integrate them to clinical variables into short and long term prognosis of COVID-19 patients.	./cache/cord-267055-xscwk74r.txt	./txt/cord-267055-xscwk74r.txt